Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics
Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in hu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fujita, Hiroaki [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Microbiome - London : Biomed Central, 2013, 11(2023), 1 vom: 29. März |
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| Übergeordnetes Werk: |
volume:11 ; year:2023 ; number:1 ; day:29 ; month:03 |
| Links: |
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| DOI / URN: |
10.1186/s40168-023-01474-5 |
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SPR049877143 |
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| 520 | |a Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. BZtjfqfgm5MWSae9EkdSwRVideo Abstract | ||
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| 650 | 4 | |a Chaos |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Community collapse |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Community stability |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Dysbiosis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Empirical dynamic modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Microbiome dynamics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Non-linear dynamics |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ushio, Masayuki |4 aut | |
| 700 | 1 | |a Suzuki, Kenta |4 aut | |
| 700 | 1 | |a Abe, Masato S. |4 aut | |
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| 700 | 1 | |a Fukushima, Keitaro |4 aut | |
| 700 | 1 | |a Fukuda, Shinji |4 aut | |
| 700 | 1 | |a Kiers, E. Toby |4 aut | |
| 700 | 1 | |a Toju, Hirokazu |4 aut | |
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10.1186/s40168-023-01474-5 doi (DE-627)SPR049877143 (SPR)s40168-023-01474-5-e DE-627 ger DE-627 rakwb eng Fujita, Hiroaki verfasserin aut Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. BZtjfqfgm5MWSae9EkdSwRVideo Abstract Alternative stable states (dpeaa)DE-He213 Biodiversity (dpeaa)DE-He213 Biological communities (dpeaa)DE-He213 Chaos (dpeaa)DE-He213 Community collapse (dpeaa)DE-He213 Community stability (dpeaa)DE-He213 Dysbiosis (dpeaa)DE-He213 Empirical dynamic modeling (dpeaa)DE-He213 Microbiome dynamics (dpeaa)DE-He213 Non-linear dynamics (dpeaa)DE-He213 Ushio, Masayuki aut Suzuki, Kenta aut Abe, Masato S. aut Yamamichi, Masato aut Iwayama, Koji aut Canarini, Alberto aut Hayashi, Ibuki aut Fukushima, Keitaro aut Fukuda, Shinji aut Kiers, E. Toby aut Toju, Hirokazu aut Enthalten in Microbiome London : Biomed Central, 2013 11(2023), 1 vom: 29. März (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:11 year:2023 number:1 day:29 month:03 https://dx.doi.org/10.1186/s40168-023-01474-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 29 03 |
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10.1186/s40168-023-01474-5 doi (DE-627)SPR049877143 (SPR)s40168-023-01474-5-e DE-627 ger DE-627 rakwb eng Fujita, Hiroaki verfasserin aut Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. BZtjfqfgm5MWSae9EkdSwRVideo Abstract Alternative stable states (dpeaa)DE-He213 Biodiversity (dpeaa)DE-He213 Biological communities (dpeaa)DE-He213 Chaos (dpeaa)DE-He213 Community collapse (dpeaa)DE-He213 Community stability (dpeaa)DE-He213 Dysbiosis (dpeaa)DE-He213 Empirical dynamic modeling (dpeaa)DE-He213 Microbiome dynamics (dpeaa)DE-He213 Non-linear dynamics (dpeaa)DE-He213 Ushio, Masayuki aut Suzuki, Kenta aut Abe, Masato S. aut Yamamichi, Masato aut Iwayama, Koji aut Canarini, Alberto aut Hayashi, Ibuki aut Fukushima, Keitaro aut Fukuda, Shinji aut Kiers, E. Toby aut Toju, Hirokazu aut Enthalten in Microbiome London : Biomed Central, 2013 11(2023), 1 vom: 29. März (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:11 year:2023 number:1 day:29 month:03 https://dx.doi.org/10.1186/s40168-023-01474-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 29 03 |
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10.1186/s40168-023-01474-5 doi (DE-627)SPR049877143 (SPR)s40168-023-01474-5-e DE-627 ger DE-627 rakwb eng Fujita, Hiroaki verfasserin aut Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. BZtjfqfgm5MWSae9EkdSwRVideo Abstract Alternative stable states (dpeaa)DE-He213 Biodiversity (dpeaa)DE-He213 Biological communities (dpeaa)DE-He213 Chaos (dpeaa)DE-He213 Community collapse (dpeaa)DE-He213 Community stability (dpeaa)DE-He213 Dysbiosis (dpeaa)DE-He213 Empirical dynamic modeling (dpeaa)DE-He213 Microbiome dynamics (dpeaa)DE-He213 Non-linear dynamics (dpeaa)DE-He213 Ushio, Masayuki aut Suzuki, Kenta aut Abe, Masato S. aut Yamamichi, Masato aut Iwayama, Koji aut Canarini, Alberto aut Hayashi, Ibuki aut Fukushima, Keitaro aut Fukuda, Shinji aut Kiers, E. Toby aut Toju, Hirokazu aut Enthalten in Microbiome London : Biomed Central, 2013 11(2023), 1 vom: 29. März (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:11 year:2023 number:1 day:29 month:03 https://dx.doi.org/10.1186/s40168-023-01474-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 29 03 |
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10.1186/s40168-023-01474-5 doi (DE-627)SPR049877143 (SPR)s40168-023-01474-5-e DE-627 ger DE-627 rakwb eng Fujita, Hiroaki verfasserin aut Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. BZtjfqfgm5MWSae9EkdSwRVideo Abstract Alternative stable states (dpeaa)DE-He213 Biodiversity (dpeaa)DE-He213 Biological communities (dpeaa)DE-He213 Chaos (dpeaa)DE-He213 Community collapse (dpeaa)DE-He213 Community stability (dpeaa)DE-He213 Dysbiosis (dpeaa)DE-He213 Empirical dynamic modeling (dpeaa)DE-He213 Microbiome dynamics (dpeaa)DE-He213 Non-linear dynamics (dpeaa)DE-He213 Ushio, Masayuki aut Suzuki, Kenta aut Abe, Masato S. aut Yamamichi, Masato aut Iwayama, Koji aut Canarini, Alberto aut Hayashi, Ibuki aut Fukushima, Keitaro aut Fukuda, Shinji aut Kiers, E. Toby aut Toju, Hirokazu aut Enthalten in Microbiome London : Biomed Central, 2013 11(2023), 1 vom: 29. März (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:11 year:2023 number:1 day:29 month:03 https://dx.doi.org/10.1186/s40168-023-01474-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 29 03 |
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10.1186/s40168-023-01474-5 doi (DE-627)SPR049877143 (SPR)s40168-023-01474-5-e DE-627 ger DE-627 rakwb eng Fujita, Hiroaki verfasserin aut Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. BZtjfqfgm5MWSae9EkdSwRVideo Abstract Alternative stable states (dpeaa)DE-He213 Biodiversity (dpeaa)DE-He213 Biological communities (dpeaa)DE-He213 Chaos (dpeaa)DE-He213 Community collapse (dpeaa)DE-He213 Community stability (dpeaa)DE-He213 Dysbiosis (dpeaa)DE-He213 Empirical dynamic modeling (dpeaa)DE-He213 Microbiome dynamics (dpeaa)DE-He213 Non-linear dynamics (dpeaa)DE-He213 Ushio, Masayuki aut Suzuki, Kenta aut Abe, Masato S. aut Yamamichi, Masato aut Iwayama, Koji aut Canarini, Alberto aut Hayashi, Ibuki aut Fukushima, Keitaro aut Fukuda, Shinji aut Kiers, E. Toby aut Toju, Hirokazu aut Enthalten in Microbiome London : Biomed Central, 2013 11(2023), 1 vom: 29. März (DE-627)734146140 (DE-600)2697425-3 2049-2618 nnns volume:11 year:2023 number:1 day:29 month:03 https://dx.doi.org/10.1186/s40168-023-01474-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 29 03 |
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Fujita, Hiroaki Ushio, Masayuki Suzuki, Kenta Abe, Masato S. Yamamichi, Masato Iwayama, Koji Canarini, Alberto Hayashi, Ibuki Fukushima, Keitaro Fukuda, Shinji Kiers, E. Toby Toju, Hirokazu |
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Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics |
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Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. BZtjfqfgm5MWSae9EkdSwRVideo Abstract © The Author(s) 2023 |
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Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. BZtjfqfgm5MWSae9EkdSwRVideo Abstract © The Author(s) 2023 |
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Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. BZtjfqfgm5MWSae9EkdSwRVideo Abstract © The Author(s) 2023 |
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Ushio, Masayuki Suzuki, Kenta Abe, Masato S. Yamamichi, Masato Iwayama, Koji Canarini, Alberto Hayashi, Ibuki Fukushima, Keitaro Fukuda, Shinji Kiers, E. Toby Toju, Hirokazu |
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However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. 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